Using self-organizing maps to classify humpback whale song units and quantify their similarity
Classification of vocal signals can be undertaken using a wide variety of qualitative and quantitative techniques. Using east Australian humpback whale song from 2002-2014, a subset of vocal signals were acoustically measured and then classified using a self-organizing map (SOM). The SOM created 1)...
Published in: | The Journal of the Acoustical Society of America |
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Online Access: | https://research-portal.st-andrews.ac.uk/en/researchoutput/using-selforganizing-maps-to-classify-humpback-whale-song-units-and-quantify-their-similarity(23c48721-dfaf-4525-88e9-4ac2d22e0631).html https://doi.org/10.1121/1.4982040 https://research-repository.st-andrews.ac.uk/bitstream/10023/13109/1/Allen_2017_Self_organizing_JASA_AAM.pdf |
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ftunstandrewcris:oai:research-portal.st-andrews.ac.uk:publications/23c48721-dfaf-4525-88e9-4ac2d22e0631 2024-06-23T07:53:35+00:00 Using self-organizing maps to classify humpback whale song units and quantify their similarity Allen, Jenny A. Murray, Anita Noad, Michael J. Dunlop, Rebecca A. Garland, Ellen Clare 2017-10-10 application/pdf https://research-portal.st-andrews.ac.uk/en/researchoutput/using-selforganizing-maps-to-classify-humpback-whale-song-units-and-quantify-their-similarity(23c48721-dfaf-4525-88e9-4ac2d22e0631).html https://doi.org/10.1121/1.4982040 https://research-repository.st-andrews.ac.uk/bitstream/10023/13109/1/Allen_2017_Self_organizing_JASA_AAM.pdf eng eng https://research-portal.st-andrews.ac.uk/en/researchoutput/using-selforganizing-maps-to-classify-humpback-whale-song-units-and-quantify-their-similarity(23c48721-dfaf-4525-88e9-4ac2d22e0631).html info:eu-repo/semantics/openAccess Allen , J A , Murray , A , Noad , M J , Dunlop , R A & Garland , E C 2017 , ' Using self-organizing maps to classify humpback whale song units and quantify their similarity ' , Journal of the Acoustical Society of America , vol. 142 , no. 4 , pp. 1943-1952 . https://doi.org/10.1121/1.4982040 Animal communication Sequence analysis Neural networks Humpback whale article 2017 ftunstandrewcris https://doi.org/10.1121/1.4982040 2024-06-13T00:56:16Z Classification of vocal signals can be undertaken using a wide variety of qualitative and quantitative techniques. Using east Australian humpback whale song from 2002-2014, a subset of vocal signals were acoustically measured and then classified using a self-organizing map (SOM). The SOM created 1) an acoustic dictionary of units representing the song’s repertoire, and 2) Cartesian distance measurements among all unit types (SOM nodes). Utilizing the SOM dictionary as a guide, additional song recordings from east Australia were rapidly (manually) transcribed. To assess the similarity in song sequences, the Cartesian distance output from the SOM was applied in Levenshtein distance similarity analyses as a weighting factor to better incorporate unit similarity in the calculation (previously a qualitative process). SOMs provide a more robust and repeatable means of categorizing acoustic signals along with a clear quantitative measurement of sound type similarity based on acoustic features. This method can be utilized for a wide variety of acoustic databases especially those containing very large datasets, and be applied across the vocalization research community to help address concerns surrounding inconsistency in manual classification. Article in Journal/Newspaper Humpback Whale University of St Andrews: Research Portal The Journal of the Acoustical Society of America 142 4 1943 1952 |
institution |
Open Polar |
collection |
University of St Andrews: Research Portal |
op_collection_id |
ftunstandrewcris |
language |
English |
topic |
Animal communication Sequence analysis Neural networks Humpback whale |
spellingShingle |
Animal communication Sequence analysis Neural networks Humpback whale Allen, Jenny A. Murray, Anita Noad, Michael J. Dunlop, Rebecca A. Garland, Ellen Clare Using self-organizing maps to classify humpback whale song units and quantify their similarity |
topic_facet |
Animal communication Sequence analysis Neural networks Humpback whale |
description |
Classification of vocal signals can be undertaken using a wide variety of qualitative and quantitative techniques. Using east Australian humpback whale song from 2002-2014, a subset of vocal signals were acoustically measured and then classified using a self-organizing map (SOM). The SOM created 1) an acoustic dictionary of units representing the song’s repertoire, and 2) Cartesian distance measurements among all unit types (SOM nodes). Utilizing the SOM dictionary as a guide, additional song recordings from east Australia were rapidly (manually) transcribed. To assess the similarity in song sequences, the Cartesian distance output from the SOM was applied in Levenshtein distance similarity analyses as a weighting factor to better incorporate unit similarity in the calculation (previously a qualitative process). SOMs provide a more robust and repeatable means of categorizing acoustic signals along with a clear quantitative measurement of sound type similarity based on acoustic features. This method can be utilized for a wide variety of acoustic databases especially those containing very large datasets, and be applied across the vocalization research community to help address concerns surrounding inconsistency in manual classification. |
format |
Article in Journal/Newspaper |
author |
Allen, Jenny A. Murray, Anita Noad, Michael J. Dunlop, Rebecca A. Garland, Ellen Clare |
author_facet |
Allen, Jenny A. Murray, Anita Noad, Michael J. Dunlop, Rebecca A. Garland, Ellen Clare |
author_sort |
Allen, Jenny A. |
title |
Using self-organizing maps to classify humpback whale song units and quantify their similarity |
title_short |
Using self-organizing maps to classify humpback whale song units and quantify their similarity |
title_full |
Using self-organizing maps to classify humpback whale song units and quantify their similarity |
title_fullStr |
Using self-organizing maps to classify humpback whale song units and quantify their similarity |
title_full_unstemmed |
Using self-organizing maps to classify humpback whale song units and quantify their similarity |
title_sort |
using self-organizing maps to classify humpback whale song units and quantify their similarity |
publishDate |
2017 |
url |
https://research-portal.st-andrews.ac.uk/en/researchoutput/using-selforganizing-maps-to-classify-humpback-whale-song-units-and-quantify-their-similarity(23c48721-dfaf-4525-88e9-4ac2d22e0631).html https://doi.org/10.1121/1.4982040 https://research-repository.st-andrews.ac.uk/bitstream/10023/13109/1/Allen_2017_Self_organizing_JASA_AAM.pdf |
genre |
Humpback Whale |
genre_facet |
Humpback Whale |
op_source |
Allen , J A , Murray , A , Noad , M J , Dunlop , R A & Garland , E C 2017 , ' Using self-organizing maps to classify humpback whale song units and quantify their similarity ' , Journal of the Acoustical Society of America , vol. 142 , no. 4 , pp. 1943-1952 . https://doi.org/10.1121/1.4982040 |
op_relation |
https://research-portal.st-andrews.ac.uk/en/researchoutput/using-selforganizing-maps-to-classify-humpback-whale-song-units-and-quantify-their-similarity(23c48721-dfaf-4525-88e9-4ac2d22e0631).html |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1121/1.4982040 |
container_title |
The Journal of the Acoustical Society of America |
container_volume |
142 |
container_issue |
4 |
container_start_page |
1943 |
op_container_end_page |
1952 |
_version_ |
1802645312241664000 |